Despite significant progress in text-to-image diffusion models, achieving precise spatial control over generated outputs remains challenging. One of the popular approaches for this task is ControlNet, which introduces an auxiliary conditioning module into the architecture. To improve alignment of the generated image and control, ControlNet++ proposes a cycle consistency loss to refine correspondence between controls and outputs, but restricts its application to the final denoising steps, while the main structure is introduced at an early generation stage. To address this issue, we suggest InnerControl — a training strategy that enforces spatial consistency across all diffusion steps. Specifically, we train lightweight control prediction probes — small convolutional networks — to reconstruct input control signals (e.g., edges, depth) from intermediate UNet features at every denoising step. We prove the efficiency of such models to extract signals even from very noisy latents and utilize these models to generate pseudo ground truth controls during training. The suggested approach enables an alignment loss that minimizes the difference between predicted and target condition throughout the whole diffusion process. Our experiments demonstrate that our method improves control alignment and fidelity of generation. By integrating this loss with established training techniques (e.g., ControlNet++), we achieve high performance across different condition methods such as edge and depth conditions.